Shadow Based Non-Line-of-Sight Pedestrian Rushing Detection for Automated Driving

Among the foremost contributors to compromised driving safety is the abrupt emergence of obstacles or pedestrians within drivers’ non-line-of-sight regions. Previous investigations into non-line-of-sight imaging have predominantly depended on costly apparatus or have been confined to controlled laboratory settings (e.g., extensive planar reflectors and regulated illumination). Consequently, these technological approaches prove impractical within intricate driving environments. In this paper, we introduce a shadow based non-line-of-sight moving obstacle detection system devised to augment Advanced Driver Assistance Systems (ADAS), ensuring adequate time for safe response and halting. Our approach incorporates a shadow signal discriminator tailored to evaluate faint shadows generated by moving obstacles, such as pedestrians within blind spots. Note that we merely use commercial onboard sensors and our system is robust to various lighting scenarios and planar reflectors. We comprehensively assess our methodology's adaptability by employing datasets acquired from real-world driving scenarios encompassing diverse road surfaces and lighting conditions. The results substantiate the system's efficacy in detecting pedestrians in motion within NLOS regions, showcasing an impressive detection range of 22 meters. This proficiency enables the system to pre-emptively forewarn the ADAS, facilitating the maintenance of a safe distance from the pedestrian.